Avoiding the Crisis du Jour

by Paul A. Keller, CQE, CQA

Itisa pretty safe guess to say that the biggest worry to your operations today may not be the same as yesterday, or last week. Crises come and go, and thankfully so. For generally, we lack the resources to properly confrontthecrisisofeach day. So when the crisis du jour vanishes, to be replaced bythepiece de resistanceof tomorrow, we canall breathe a sigh of relief and pat ourselves on the back for putting yet another nightmare behind us. Until it comes back to haunt us once again - a leftover du jour, so to speak. Then, of course, its much easier to "solve," since we just remember who got yelled at last time, and figure they need a reminder.

If this sounds too familiar, you have plenty of company. In fact, these engagements are sold out for months in most organizations. What is missing here is the correct approach to problem solving and analysis. For starters, we need to avoid confronting every crisis dujour as if it were indicative of something in and of itself. Many times, itis not.

How can I say this? Because, as a general rule, common cause variation is more prevalent than special cause variation. To deal correctly with the crisis de jour, we must first understand if itis induced by a common cause or a special cause. If related to a special cause, we should pay close attention to the details of its occurrence, since they represent conditions of the process specific to that point in time. If related to a common cause, we should address the system that produces it. As a common cause, the variation evidenced by the crisis in question is an inherent part of the behavior of the process. Ifwe treat a common cause like a special cause, our tampering would tend to increase the amount of variation in the process. (See Deming, Out of the Crisis).

Consider an example.Thestaff meetingon Monday morningturns into a free for all becausetheerror ratefor last week- or infection rate, scrap rate, non-conformance rate (substitute your keyprocessparameter here)- went to hell in a hand basket (or a quicker mode of transport).While ittypicallysitsat 3%, last week saw an unprecedented 4.2%. Andpeople want some answers. NOW! Well, before we start changing our hiring, training, or disciplinary practices, some analysiswould be warrantedto see if this rate is noteworthy or not.

Charting this key parameter over the course of time on ap chart, we find that its average is indeed 3%. We should expect to have a larger rate than this 50% of the time, and a lower rate than this 50% of the time, with limits that vary depending on the sample size (# of pieces, patients, records audited, etc.), assuming our process is stable.Based on thevolumeexperienced last weekof 1000 units, if our process is stable, we should expect it to operate between 1.4% and 4.6% error rate. Any variation within these limits tells us the process has not varied,that it is driven by only the common causes which are inherent to the system. Knowing this, we shouldnot pat ourselves on the back for the non-existent "reduction" to 2% that occurred last month, nor search for the elusive cause ofitspredictable "crisis-level" of 4.2%.

Using the control chart, we now recognize that the process itself must be improved to reduce the error rate. At this point, we have several options available to us. ACause and Effect diagramwould probably be useful to map out the potential sources of common cause variation. (Consider the 5M and E: methods, materials, manpower, machines, measurement and environment).

However, to properly brainstorm on theses causes of process variation, we should map out the sequential process tasks using a Flowchart. A Flowchart will allow us to appreciate the complexities of the process, which should help in identifying the potential causes of variation at each step. In may be that some paths are not necessary, or do nothing to improve the customer experience, and can be removed. In other cases, paths may lead to undesirable conclusions (such as customer dissatisfaction) and the process would have to be re-designed to prevent this occurrence.

The interdependence of the potential causes identified in theCause and Effect Diagramcan be identified using theInterrelationship Digraph(ID, one of the 7 Management and Planning tools). The ID is particularly useful for identifying root causes which contribute toother sources of variation. AProcess Decision Program Chart(PDPC, another 7MP tool) can be used graphically depict various alternatives and countermeasures associated with a process.

Since 1982: The art & science to improve your bottom line

Quality America
offers Statistical Process Control software, as well as training materials for Lean Six
Sigma, Quality Management and SPC. We embrace a customer-driven approach, and lead in
many software innovations, continually seeking ways to provide our customers with the
best and most affordable solutions. Leaders in their field, Quality America has provided
software and training products and services to tens of thousands of companies in over
25 countries.